Internalizing Global Value Chains: A Firm-Level Analysis

Transcription

1 Internalizing Global Value Chains: A Firm-Level Analysis Laura Alfaro Harvard Business School Pol Antràs Harvard University Davin Chor National University of Singapore Paola Conconi Université Libre de Bruxelles (ECARES) October 2017 Abstract In recent decades, advances in information and communication technology and falling trade barriers have led firms to retain within their boundaries and in their domestic economies only a subset of their production stages. A key decision facing firms worldwide is the extent of control to exert over the different segments of their production processes. We describe a property-rights model of firm boundary choices along the value chain that generalizes Antràs and Chor (2013). To assess the evidence, we construct firm-level measures of the upstreamness of integrated and non-integrated inputs by combining information on the production activities of firms operating in more than 100 countries with Input-Output tables. In line with the model s predictions, we find that whether a firm integrates upstream or downstream suppliers depends crucially on the elasticity of demand for its final product. Moreover, a firm s propensity to integrate a given stage of the value chain is shaped by the relative contractibility of the stages located upstream versus downstream from that stage, as well as by the firm s productivity. Our results suggest that contractual frictions play an important role in shaping the integration choices of firms around the world. JEL classifications: F14, F23, D23, L20. Keywords: Global value chains, sequential production, incomplete contracts. We thank participants at the following conferences: ERWIT, Barcelona GSE Summer Forum, Princeton IES Summer Workshop, NBER ITI and OE meetings, ETSG, Asia Pacific Trade Seminars, the AEA meetings, the EEA meetings, the Global Fragmentation of Production and Trade Policy conference (ECARES), the World Bank GVC conference, the World Bank Kuala Lumpur conference, and the Trade and Macro Interdependence in the Age of GVCs conference (Lithuania). In addition, we thank seminar audiences at LSE, the Paris Trade Workshop, MIT Sloan, Boston College, Warwick, Ferrara, Munich, Sapienza, Bologna, Nottingham, Bank of Italy, HKUST, HKU, NUS, SMU, UIBE, Nottingham Ningbo, and University of Tokyo. We are particularly grateful to Kamran Bilir, Arnaud Costinot, Thibault Fally, Silke Forbes, Thierry Mayer, Peter Morrow, Claudia Steinwender, David Weinstein, three anonymous referees and the editor (Ali Hortaçsu) for their detailed comments. Chor thanks the Global Production Networks Centre (GPN@NUS) for funding support. Alfaro: lalfaro@hbs.edu. Antràs: pantras@fas.harvard.edu. Chor: davinchor@nus.edu.sg. Conconi: pconconi@ulb.ac.be.

2 1 Introduction Sequential production has been an important feature of modern manufacturing processes at least since Henry Ford introduced his Model T assembly line in The production of cars, computers, mobile phones and most other manufacturing goods involves a sequencing of stages: raw materials are converted into basic components, which are then combined with other parts to produce more complex inputs, before being assembled into final goods. In recent decades, advances in information and communication technology and falling trade barriers have led firms to retain within their boundaries and in their domestic economies only a subset of these production stages. Research and development, design, production of parts, assembly, marketing and branding, previously performed in close proximity, are increasingly fragmented across firms and countries. 1 While fragmenting production across firms and countries has become easier, contractual frictions remain a significant obstacle to the globalization of value chains. On top of the inherent difficulties associated with designing richly contingent contracts, international transactions suffer from a disproportionately low level of enforcement of contract clauses and legal remedies (Antràs, 2015). In such an environment, companies are presented with complex organizational choices. In this paper, we focus on a key decision faced by firms worldwide: the extent of control to exert over the different segments of their production process. Although the global fragmentation of production has featured prominently in the trade literature (e.g., Johnson and Noguera, 2012), much less attention has been placed on how the position of a given production stage in the value chain affects firm boundary choices, and firm organizational decisions more broadly. Most studies on this topic have been mainly theoretical in nature. 2 large extent, this theoretical bias is explained by the challenges one faces when taking models of global value chains to the data. Ideally, researchers would like to access comprehensive datasets that would enable them to track the flow of goods within value chains across borders and organizational forms. Trade statistics are useful in capturing the flows of goods when they cross a particular border, and some countries customs offices also record whether goods flow in and out of a country within or across firm boundaries. Nevertheless, once a good leaves a country, it is virtually impossible with available data sources to trace the subsequent locations (beyond its first immediate destination) where the good will be combined with other components and services. The first contribution of this paper is to show how available data on the activities of firms can be combined with information from standard Input-Output tables to study firm boundaries along value chains. A key advantage of this approach is that it allows us to study how the integration of stages in a firm s production process is shaped by the characteristics in particular, the production 1 The semiconductor industry exemplifies these trends. The first semiconductor chips were manufactured in the United States by vertically integrated firms such as IBM and Texas Instruments. Firms initially kept the design, fabrication, assembly, and testing of integrated circuits within ownership boundaries. The industry has since undergone several reorganization waves, and many of the production stages are now outsourced to independent contractors in Asia (Brown and Linden, 2005). Another example is the iphone: while its software and product design are done by Apple, most of its components are produced by independent suppliers around the world (Xing, 2011). 2 See, among others, Dixit and Grossman (1982), Yi (2003), Baldwin and Venables (2013), Costinot et al. (2013), Antràs and Chor (2013), Fally and Hillberry (2014), Kikuchi et al. (2017), and Antràs and de Gortari (2017). To a 1

3 line position (or upstreamness ) of these different stages. Moreover, the richness of our data allows us to run specifications that exploit variation in organizational features across firms, as well as within firms across their various inputs. Available theoretical frameworks of sequential production are highly stylized and often do not feature asymmetries across production stages other than in their position along the value chain. A second contribution of this paper is to develop a richer framework of firm behavior that can closely guide our firm-level empirical analysis. Toward this end, we build on the property-rights model in Antràs and Chor (2013), by generalizing it to an environment that accommodates differences across input suppliers along the value chain on the technology and cost sides. 3 We focus on the problem of a firm controlling the manufacturing process of a final-good variety, which is associated with a constant price elasticity demand schedule. The production of the final good entails a large number of stages that need to be performed in a predetermined order. The different stage inputs are provided by suppliers, who each undertake relationship-specific investments to make their components compatible with those of other suppliers along the value chain. The setting is one of incomplete contracting, in the sense that contracts contingent on whether components are compatible or not cannot be enforced by third parties. As a result, the division of surplus between the firm and each supplier is governed by bargaining, after a stage has been completed and the firm has had a chance to inspect the input. The firm must decide which input suppliers (if any) to own along the value chain. As in Grossman and Hart (1986), the integration of suppliers does not change the space of contracts available to the firm and its suppliers, but it affects the relative ex-post bargaining power of these agents. A key feature of our model is that organizational decisions have spillovers along the value chain because relationship-specific investments made by upstream suppliers affect the incentives of suppliers in downstream stages. Perhaps surprisingly, we show that the key predictions of Antràs and Chor (2013) continue to hold in this richer environment with input asymmetries. In particular, a firm s decision to integrate upstream or downstream suppliers depends crucially on the relative size of the elasticity of demand for its final good and the elasticity of substitution across production stages. When demand is elastic or inputs are not particularly substitutable, inputs are sequential complements, in the sense that the marginal incentive of a supplier to undertake relationship-specific investments is higher, the larger are the investments by upstream suppliers. In this case, the firm finds it optimal to integrate only the most downstream stages, while contracting at arm s length with upstream suppliers in order to incentivize their investment effort. When instead demand is inelastic or inputs are sufficiently substitutable, inputs are sequential substitutes, and the firm would choose to integrate relatively upstream stages, while engaging in outsourcing to downstream suppliers. While the profile of marginal productivities and costs along the value chain does not detract from this core prediction, it does shape the measure of stages (i.e., how many inputs) the firm ends up finding optimal to integrate in both the complements and the substitutes cases. We develop several extensions of the model that are relevant for our empirical analysis. First, 3 The property-rights approach builds on the seminal work of Grossman and Hart (1986), and has been employed to study the organization of multinational firms. See Antràs (2015) for a comprehensive overview of this literature. 2

4 we map the asymmetries across inputs to differences in their inherent degree of contractibility. We show that the propensity of a firm to integrate a given stage is shaped in subtle ways by the contractibility of upstream and downstream stages. Second, we incorporate heterogeneity across final good producers in their core productivity, while introducing fixed costs of integrating suppliers, as in Antràs and Helpman (2004). We show how such productivity differences influence the number of stages that are integrated, and hence the propensity of the firm to integrate upstream relative to downstream stages. Finally, we consider a scenario in which integration is infeasible for certain segments of the value chain, for example, due to exogenous technological or regulatory factors. We show that even when integration is sparse (as is the case in our data), the model s predictions continue to describe firm boundary choices for those inputs over which integration is feasible. To assess the validity of the model s predictions, we employ the WorldBase dataset of Dun and Bradstreet (D&B), an establishment-level database covering public and private companies in many countries. For each establishment, WorldBase reports a list of up to six production activities, together with ownership information that allows us to link establishments belonging to the same firm. Our main sample consists of more than 300,000 manufacturing firms in 116 countries. In our empirical analysis, we study the determinants of a firm s propensity to integrate upstream versus downstream inputs. To distinguish between integrated and non-integrated inputs, we rely on the methodology of Fan and Lang (2000), combining information on firms reported activities with Input-Output tables (see also Acemoglu et al., 2009; and Alfaro et al., 2016). To capture the position of different inputs along the value chain, we compute a measure of the upstreamness of each input i in the production of output j using U.S. Input-Output Tables. This extends the measure of the upstreamness of an industry with respect to final demand from Fally (2012) and Antràs et al. (2012) to the bilateral industry-pair level. To provide a test of the model, we exploit information from WorldBase on the primary activity of each firm, and use estimates of demand elasticities from Broda and Weinstein (2006), as well as measures of contractibility from Nunn (2007). We first examine how firms organizational choices depend on the elasticity of demand for their final good. In line with the first prediction of the model, we find that the higher the elasticity of demand faced by the parent firm, the lower the average upstreamness of its integrated inputs relative to the upstreamness of its non-integrated inputs. This result is illustrated in a simple (unconditional) form in Figure 1, based on different quintiles of the parent firm s elasticity of demand. As seen in the left panel of the figure, the average upstreamness of integrated inputs is much higher when the parent company belongs to an industry with a low demand elasticity than when it belongs to one associated with a high demand elasticity. Conversely, the right panel shows that the average upstreamness of non-integrated stages is greater the higher the elasticity of demand faced by the parent s final good. 4 4 Figure 1 is plotted using only inputs i that rank within the top 100 manufacturing inputs in terms of total requirements coefficients of the parent s output industry j. The average for each firm is computed weighting each input by its total requirements coefficient tr ij, while excluding integrated stages belonging to the same industry j as the parent; a simple unweighted average across firms in the elasticity quintile is then illustrated. The figures obtained when considering all manufacturing inputs, when computing unweighted averages over inputs, and when considering the output industry j as an input, are all qualitatively similar. 3

5 Figure 1: Average Upstreamness of Production Stages, by Quintile of Parent s Demand Elasticity (a) Integrated Stages (b) Non Integrated Stages Q1 Q2 Q3 Q4 Q5 1.3 Q1 Q2 Q3 Q4 Q5 The above pattern is robust in the regression analysis, even when controlling for a comprehensive list of firm characteristics (e.g., size, age, employment, sales), using different measures of the demand elasticity, as well as in different subsamples of firms (e.g., restricting to domestic firms, or to multinationals). We also show that our results hold in specifications where the elasticity of demand is replaced by the difference between this same elasticity and a proxy for the degree of input substitutability associated with the firm s production process. We reach a similar conclusion when we exploit within-firm variation in integration patterns. In these specifications, we find that a firm s propensity to integrate is generally lower for more upstream inputs (consistent with the smaller bars observed in the left panel relative to the right panel of Figure 1), and that the negative effect of upstreamness on integration is disproportionately large for firms facing high demand elasticities. We report two further empirical regularities that are strongly consistent with the model s implications. First, we find that firms ownership decisions are shaped by the contractibility of upstream versus downstream inputs: a greater degree of upstream contractibility increases the likelihood that a firm integrates upstream inputs, when the firm faces a high elasticity of demand (i.e., in the complements case); conversely, it increases the propensity to outsource upstream inputs, when the firm s demand elasticity is low (i.e., in the substitutes case). Intuitively, when production features a high degree of upstream contractibility, firms need to rely less on the organizational mode to counteract the distortions associated with inefficient investments upstream. Hence, high levels of upstream contractibility tend to reduce the set of outsourced stages when inputs are sequential complements, while reducing the set of integrated stages when inputs are sequential substitutes. 5 Second, we find that more productive firms integrate more inputs in industries across all the demand elasticity quintiles. This implies that more productive firms will exhibit a higher propensity 5 The somewhat counterintuitive positive effect of contractibility on integration is a recurrent result in the propertyrights literature. For instance, Baker and Hubbard (2004) document that improvements in the contracting environment in the trucking industry (through the use of on-board computers) led to more integrated asset ownership. In international trade settings, Nunn and Trefler (2008), Defever and Toubal (2013), and Antràs (2015) have documented a similar positive association between contractibility and vertical integration. 4

6 to integrate relatively downstream (respectively, upstream) inputs when the elasticity of demand for their final product is low (respectively, high); this is exactly what we uncover in the data. This set of findings suggests that contractual frictions play a key role in shaping the integration choices of firms around the world. The rich differential effects observed in the complements and substitutes cases are consistent with a view of integration choices that is rooted in the propertyrights approach to the theory of the firm, though we do not rule out that these could possibly be rationalized by alternative theories (as we briefly discuss in the concluding section). It is also useful to describe how our analysis relates to other recent work on vertical linkages at the firm level. In an influential study, Atalay et al. (2014) find little evidence of intrafirm shipments between related plants within the United States; they instead present evidence indicating that firm boundaries are more influenced by the transfer of intangible inputs, than by the transfer of physical goods. Our theory is abstract enough to allow one to interpret the sequential investments as resulting in either tangible or intangible transfers across establishments; and our empirical analysis takes into account both manufacturing and non-manufacturing inputs (including services). That said, due to the inherent difficulties in measuring intangible inputs, we believe that our empirical results speak more to the optimal provision of incentives along sequential value chains involving tangible inputs. 6 Relatedly, our analysis suggests that intrafirm trade flows are an imperfect proxy for the extent to which firms react to contractual insecurity by internalizing particular stages of their global value chains. As the sparse integration extension of our model shows, internalization decisions along value chains are consistent with an arbitrarily low level of intrafirm trade relative to the overall transaction volume in these chains. This helps reconcile our findings with Ramondo et al. (2016), who find intrafirm trade between U.S. multinationals and their affiliates abroad to be highly concentrated among a small number of large affiliates. Our work is closely related to two contemporaneous firm-level empirical investigations of the Antràs and Chor (2013) model. Del Prete and Rungi (2017) employ a dataset of about 4,000 multinational business groups to explore the correlation between the average downstreamness of integrated affiliates and that of the parent firm itself (both measured relative to final demand). Luck (2016) reports corroborating evidence based on the city-level value-chain position of processing export activity in China. More generally, our paper is related to a recent empirical literature testing various aspects of the property-rights theory of multinational firm boundaries. 7 The remainder of the paper is organized as follows. Section 2 presents our model of firm boundaries with sequential production and input asymmetries. Section 3 describes the data. Section 4 outlines our empirical methodology and presents our findings. Section 5 concludes. The Online Appendix contains additional material related to both the theory and the empirical analysis. 6 It is important to stress, however, that our findings should not be interpreted as invalidating the intangibles hypothesis. In fact, we will report some patterns in the data which are suggestive of an efficiency-enhancing role of the common ownership of proximate product lines. 7 This includes Antràs (2003), Yeaple (2006), Nunn and Trefler (2008, 2013), Corcos et al. (2013), Defever and Toubal (2013), Díez (2014), and Antràs (2015), among others. Our work is also related to the broader empirical literature on firm boundaries; see Lafontaine and Slade (2007) and Bresnahan and Levin (2012) for overviews. 5

7 2 Theoretical Framework In this section, we develop our model of sequential production. We first describe a generalized version of the model in Antràs and Chor (2013) that incorporates heterogeneity across inputs beyond their position along the value chain. We then consider three extensions to derive additional theoretical results and enrich the set of predictions that can be brought to the data. 2.1 Benchmark Model with Heterogeneous Inputs We focus throughout on the problem of a firm seeking to optimally organize a manufacturing process that culminates in a finished good valued by consumers. The final good is differentiated in the eyes of consumers and belongs to a monopolistically competitive industry with a continuum of active firms, each producing a differentiated variety. Consumer preferences over the industry s varieties feature a constant elasticity of substitution, so that the demand faced by the firm in question is: q = Ap 1/(1 ρ), (1) where A > 0 is a term that the firm takes as given, and the parameter ρ (0, 1) is positively related to the degree of substitutability across final-good varieties. Note that A is allowed to vary across firms in the industry (perhaps reflecting differences in quality), while the demand elasticity 1/ (1 ρ) is common for all firms in the sector. The latter assumption is immaterial for our theoretical results, but will be exploited in the empirical implementation, where we rely on sectoral estimates of demand elasticities. Given that we largely focus on the problem of a representative firm, we abstain from indexing variables by firm or sector to keep the notation tidy. Obtaining the finished product requires the completion of a unit measure of production stages. These stages are indexed by i [0, 1], with a larger i corresponding to stages further downstream and thus closer to the finished product. Denote by x(i) the value of the services of intermediate inputs that the supplier of stage i delivers to the firm. Final-good production is then given by: ( 1 1/α q = θ (ψ (i) x(i)) α I (i) di), (2) 0 where θ is a productivity parameter, α (0, 1) is a parameter that captures the (symmetric) degree of substitutability among the stage inputs, the shifters ψ (i) reflect asymmetries in the marginal product of different inputs investments, and I (i) is an indicator function that takes a value of 1 if input i is produced after all inputs i < i have been produced, and a value of 0 otherwise. The technology in (2) resembles a conventional CES production function with a continuum of inputs, but the indicator function I (i) makes the production technology inherently sequential. Intermediate inputs are produced by a unit measure of suppliers, with the mapping between inputs and suppliers being one-to-one. Inputs are customized to make them compatible with the needs of the firm controlling the finished product. In order to provide a compatible input, the 6

8 supplier of input i must undertake a relationship-specific investment entailing a marginal cost of c(i) per unit of input services x (i). All agents including the firm are capable of producing subpar inputs at a negligible marginal cost, but these inputs add no value to final-good production apart from allowing the continuation of the production process in situations in which a supplier threatens not to deliver his or her input to the firm. If the firm could discipline the behavior of suppliers via a comprehensive ex-ante contract, those threats would be irrelevant. For instance, the firm could demand the delivery of a given volume x (i) of input services in exchange for a fee, while including a clause in the contract that would punish the supplier severely when failing to honor this contractual obligation. In practice, however, a court of law will generally not be able to verify whether inputs are compatible or not. For the time being, we will make the stark assumption that none of the aspects of input production can be specified in a binding manner in an initial contract, except for a clause stipulating whether the different suppliers are vertically integrated into the firm or remain independent. Because the terms of exchange between the firm and the suppliers are not set in stone before production takes place, the actual payment to a supplier (say the one controlling stage i) is negotiated bilaterally only after the stage i input has been produced and the firm has had a chance to inspect it. At that point, the firm and the supplier negotiate over the division of the incremental contribution to total revenue generated by supplier i. The lack of an enforceable contract implies that suppliers can set the volume of input services x (i) to maximize their payoff conditional on the value of the semi-finished good they are handed by their immediate upstream supplier. How does integration affect the game played between the firm and the unit measure of suppliers? Following the property-rights theory of firm boundaries, we let the effective bargaining power of the firm vis-à-vis a supplier depend on whether the firm owns this supplier. Under integration, the firm controls the physical assets used in the production of the input, thus allowing the firm to dictate a use of these assets that tilts the division of surplus in its favor. We capture this central insight of the property-rights theory in a stark manner, with the firm obtaining a share β V of the value of supplier i s incremental contribution to total revenue when the supplier is integrated, while receiving only a share β O < β V of that surplus when the supplier is a stand-alone entity. This concludes the description of the setup of the model. Figure 2 summarizes the timing of events of the game played by the firm and the unit measure of suppliers. Despite the presence of additional sources of input asymmetries, captured by the functions ψ (i) and c(i), the subgame perfect equilibrium of the above game can be derived in a manner similar to Antràs and Chor (2013). We begin by noting that, if all suppliers provide compatible inputs and the correct technological sequencing of production is followed, equations (1) and (2) imply that the total revenue obtained by the firm is given by r (1), where the function r (m) is defined by: ( m ρ/α r (m) = A 1 ρ θ ρ (ψ (i) x(i)) di) α. (3) 0 Because the firm can always unilaterally complete a production stage by producing a subpar input 7

9 Figure 2: Timing of Events i 5 i 6 i 7 i 4 i 8 i 3 i 9 i 2 i 1 i=0 i=1 i 11 i 10 t 0 t 1 t 2 t 3 Firm posts contracts for each stage i [0,1] Contract states whether i is integrated or not Suppliers apply and the firm selects one supplier for each i Sequential production. At each stage i: the supplier is handed the semifinished good completed up to i; after observing its value, the supplier chooses an input level, x(i); After observing x(i), the firm and supplier bargain over the supplier s addition to total revenue Final good assembled and sold to consumers at negligible cost, one can interpret r (m) as the revenue secured up to stage m. Now consider the bargaining between the firm and the supplier at stage m. Because inputs are customized to the needs of the firm, the supplier s outside option at the bargaining stage is 0 and the quasi-rents over which the firm and the supplier negotiate are given by the incremental contribution to total revenue generated by supplier m at that stage. 8 Applying Leibniz rule to (3), this is given by: r (m) = ρ α ( A 1 ρ θ ρ) α ρ r(m) ρ α ρ ψ (m) α x(m) α. (4) As explained above, in the bargaining, the firm captures a share β (m) {β V, β O } of r (m), while the supplier obtains the residual share 1 β (m). It then follows that the choice of input volume x(m) is characterized by the program: { x (m) = arg max (1 β (m)) ρ ( A 1 ρ θ ρ) } α ρ r (m) ρ α ρ ψ (m) α x(m) α c (m) x(m). (5) x(m) α Notice that the marginal return to investing in x (m) is increasing in the demand level A, while it decreases in the marginal cost c(m). Furthermore, this marginal return is increasing in supplier m s bargaining share 1 β (m), and thus, other things equal, outsourcing provides higher-powered incentives for the supplier to invest. This is a standard feature of property-rights models. The more novel property of program (5) is that a supplier s marginal return to invest at stage m is shaped by all investment decisions in prior stages, i.e., {x(i)} m i=0, as captured by the value of production secured up to stage m, i.e., r (m). The nature of such dependence is in turn crucially shaped by the relative size of the demand elasticity parameter ρ and the input substitutability parameter α. When ρ > α, investment choices are sequential complements in the sense that higher investment levels by 8 Antràs and Chor (2013) provide an extensive discussion of the robustness of the key results should ex-ante transfers between the firm and the suppliers be allowed, and under alternative bargaining protocols that allow supplier i to lay claim over part of the revenues that are realized downstream of i. 8

10 upstream suppliers increase the marginal return of supplier m s own investment. Conversely, when ρ < α, investment choices are sequential substitutes because high values of upstream investments reduce the marginal return to investing in x(m). We shall refer to ρ > α as the complements case and to ρ < α as the substitutes case, as in Antràs and Chor (2013). It is intuitively clear why low values of α will tend to render investments sequential complements. Why might a low value of ρ render investments sequential substitutes? The reason for this is that when ρ is low, the firm s revenue function is highly concave in output and thus marginal revenue falls at a relatively fast rate along the value chain. As a result, the incremental contribution to revenue associated with supplier m which is what the firm and supplier m bargain over might be particularly low when upstream suppliers have invested large amounts. We now plug the first-order condition from (5) into (4), and solve the resulting separable differential equation. As shown in Section A-1 of the Online Appendix, one can express the equilibrium volume of input m services x (m) as a function of the whole path of bargaining shares {β (i)} i [0,m] up to stage m: x (m) = Aθ ρ 1 ρ ( ) ρ α 1 ρ α(1 ρ) 1 ρ 1 ρ 1 α ( 1 β (m) c (m) ) 1 1 α ψ (m) α 1 α [ m ( (1 β (i)) ψ (i) 0 c (i) ) α 1 α di ] ρ α α(1 ρ). It is then straightforward to see that x (m) > 0 for all m as long as β (m) < 1. This in turn implies that the firm has every incentive to abide by the proper (or technological) sequencing of production, so that I (m) = 1 for all m (consistent with our expressions above). Next, we roll back to the initial period prior to any production taking place, in which the firm decides whether the contract associated with a given input m is associated with integration or outsourcing. This amounts to choosing {β (i)} i [0,1] to maximize π F = 1 0 β(i)r (i)di, with r (m) given in equation (4), x (m) in equation (6), and β (i) {β V, β O }. After several manipulations, the problem of choosing the optimal organizational structure can be reduced to the program: (6) max β(i) π F = Θ ( ) α 1 0 β(i) (1 β(i))ψ(i) 1 α c(i) s.t. β (i) {β V, β O }, [ i 0 ( ) α ] ρ α α(1 ρ) (1 β(k))ψ(k) 1 α c(k) dk di (7) where Θ = Aθ ρ ( 1 ρ ρ 1 ρ α 1 α ) ρ α α(1 ρ) ρ ρ 1 ρ > 0. It will prove useful to consider a relaxed version of program (7) in which rather than constraining β (i) to equal β V or β O, we allow the firm to freely choose the function β(i) from the whole set of piecewise continuously differentiable real-valued functions. Defining: v (i) i 0 ( (1 β (k)) ψ (k) c (k) ) α 1 α dk, (8) we can then turn this relaxed program into a calculus of variation problem where the firm chooses 9

11 the real-value function v that maximizes: π F (v) = Θ 1 0 ( 1 v (i) 1 α α ) c (i) v (i) v (i) ρ α α(1 ρ) di. (9) ψ (i) In Section A-1 of the Online Appendix, we show that imposing the necessary Euler-Lagrange and transversality conditions, and after a few cumbersome manipulations, the optimal (unrestricted) division of surplus at stage m can be expressed as: [ m α β 0 (ψ (k) /c (k)) 1 α dk (m) = 1 α α (ψ (k) /c (k)) 1 α dk 1 0 ] α ρ α. (10) The term inside the square brackets is a monotonically increasing function of m. This confirms the claim in Antràs and Chor (2013) that whether the optimal division of surplus increases or decreases along the value chain is shaped critically by the relative size of the parameters α and ρ. 9 In the complements case (ρ > α), the incentive to integrate suppliers increases as we move downstream in the value chain. Intuitively, given sequential complementarity, the firm is particularly concerned about incentivizing upstream suppliers to raise their investment effort, in order to generate positive spillovers on the investment levels of downstream suppliers. Instead, in the substitutes case (ρ < α), the firm is less concerned with underinvestment by upstream suppliers, while capturing rents upstream is particularly appealing when marginal revenue falls quickly with output. A remarkable feature of equation (10) is that the slope of β (m) / m is governed by the sign of ρ α regardless of the paths of ψ (k) and c (k). It is worth pausing to explain why this result is not straightforward. Note that a disproportionately high value of ψ (m) at a given stage m can be interpreted as that stage being relatively important in the production process. 10 According to one of the canonical results of the property-rights literature, one would then expect the incentive to outsource such a stage to be particularly large (see, in particular, Proposition 1 in Antràs, 2014). Intuitively, outsourcing provides higher-powered incentives to suppliers, and minimizing underinvestment inefficiencies is particularly beneficial for inputs that are relatively important in production. One might have thus expected the optimal division of surplus β (m) to be decreasing in stage m s importance ψ (m). For the same reason, and given that input shares are monotonically decreasing in the marginal cost c (m), one might have also expected the share β (m) to be increasing in c (m). One would then be led to conclude that if the path of ψ (m) were sufficiently increasing in m or the path of c (m) were sufficiently decreasing in m then β (m) would tend to decrease along the value chain, particularly when the difference between ρ and α is small. Equation (10) demonstrates, however, that this line of reasoning is flawed. No matter by how little ρ and α differ, the slope of β (m) is uniquely pinned down by the sign of ρ α, regardless 9 Although Antràs and Chor (2013) considered a variant of their model with heterogeneity in ψ (i) and c (i), they failed to derive this explicit formula for β (m) and simply noted that β (m) / m inherited the sign of ρ α (see, in particular, equation (28) in their paper). 10 Indeed, in a model with complete contracts, the share of m in the total input purchases of the firm would be a monotonically increasing function of ψ (m). 10

12 of the paths of ψ (m) and c (m). This result bears some resemblance to the classic result in consumption theory that an agent s dynamic utility-maximizing level of consumption should be growing or declining over time according to whether the real interest rate is greater or smaller than the rate of time preference, regardless of the agent s income path. It is important to stress, however, that the paths of ψ (m) and c (m) are not irrelevant for the incentive to integrate suppliers along the value chain (in the same manner that the path of income is not irrelevant in the dynamic consumption problem). Equation (10) illustrates that the incentives to integrate a particular input will be notably shaped by the size of the ratio ψ (k) /c (k) for inputs upstream from input m relative to the average size of this ratio along the whole value chain. More specifically, in production processes featuring sequential complementarity, the higher is the value of ψ (k) /c (k) for inputs upstream from m relative to its value for inputs downstream from m, the higher will be the incentive of the firm to integrate stage m. The intuition behind this result is as follows. Remember that when inputs are sequential complements, the marginal incentive of supplier m to invest will be higher, the higher are the levels of investment by suppliers upstream from m. Furthermore, fixing the ownership structure, these upstream investments will also tend to be relatively large whenever stages m upstream from m are associated with disproportionately large values of ψ (m ) or low values of c (m ). In those situations, and due to sequential complementarity, the incentives to invest at stage m will also tend to be disproportionately large, and thus the incentive of the firm to outsource stage m will be reduced relative to a situation in which the ratio ψ (k) /c (k) is common for all stages. Conversely, whenever ρ < α, investments are sequential substitutes, and thus high upstream investments related to disproportionately high upstream values of ψ (m ) /c (m ) for m < m will instead increase the likelihood that stage m is outsourced. So far, we have focused on a characterization of the optimal bargaining share β (m), but the above results can easily be turned into statements regarding the propensity of firms to integrate (β (m) = β V ) or outsource (β (m) = β O ) the different stages along the value chain. In particular, in Section A-1 of the Online Appendix, we show that: Proposition 1. In the complements case (ρ > α), there exists a unique m C (0, 1], such that: (i) all production stages m [0, m C ) are outsourced; and (ii) all stages m [m C, 1] are integrated within firm boundaries. In the substitutes case (ρ < α), there exists a unique m S (0, 1], such that: (i) all production stages m [0, m S ) are integrated within firm boundaries; and (ii) all stages m [m S, 1] are outsourced. Furthermore, both m C and m S are lower, the higher is the ratio ψ (m) /c (m) for upstream inputs relative to downstream inputs. Figure 3 illustrates the main result in Proposition 1 concerning the optimal pattern of ownership along the value chain. When the demand faced by the final-good producer is sufficiently elastic, then there exists a unique cutoff stage such that all inputs prior to that cutoff are outsourced, and all inputs (if any) downstream of it are integrated. The converse prediction holds when demand is sufficiently inelastic (i.e., in the sequential substitutes case): the firm would instead integrate relatively upstream inputs, while outsourcing would take place relatively downstream. Although the last statement in Proposition 1 follows pretty immediately from our discussion of 11

13 Figure 3: Firm Boundary Choices along the Value Chain Sequential complements: Sequential substitutes: Outsource m C * Integrate Integrate m S * Outsource the properties of the solution β (m) to the relaxed problem, it can also be shown more directly by explicitly characterizing the thresholds m C and m S. For the sequential complements case, we show in Section A-1 of the Online Appendix that, provided that integration and outsourcing coexist along the value chain, the threshold m C is given by: m C 0 (ψ (k) /c (k)) α α dk (ψ (k) /c (k)) α 1 α dk = 1 + ( 1 βo 1 β V ) α 1 α 1 β O βv ( 1 1 βo 1 β V ) α 1 α α(1 ρ) ρ α 1 1. (11) Notice then that the larger the value of ψ (k) /c (k) in upstream production stages (in the numerator of the left-hand side) relative to downstream production stages, the lower will be the value of m C ; the set of integrated stages will thus be larger. 11 (The analogous expression for m S in the substitutes case is reported in Section A-1 of the Online Appendix.) 2.2 Extensions A. Heterogeneous Contractibility of Inputs In order to develop empirical tests of Proposition 1 and especially its last statement it is important to map variation in the ratio ψ (m) /c (m) along the value chain to certain observables. With that in mind, in this section we explore the link between ψ (m) and the degree of contractibility of different stage inputs. In Section A-1 of the Online Appendix, we also briefly relate marginal cost variation in c (m) along the value chain to the sourcing location decisions of the firm. 12 Remember that in our benchmark model, x (m) captures the services related to the noncontractible aspects of input production, in the sense that the volume x (m) cannot be disciplined via an initial contract and is chosen unilaterally by suppliers. Conversely, we shall now assume that ψ (m) encapsulates investments and other aspects of production that are specified in the initial contract in a way that precludes any deviation from that agreed level. In light of equation (2), 11 In the complements case, integration and outsourcing coexist along the value chain when β V (1 β V ) α 1 α > β O (1 β O) 1 α α, which ensures m C < 1. When instead β V (1 β V ) 1 α α < β O (1 β O) 1 α α, the firm finds it optimal to outsource all stages, i.e., m C = In the absence of contractual frictions, ψ (m) /c (m) would be positively related to the relative use of input m in the production of the firm s good, and one could presumably use information from Input-Output tables to construct empirical proxies for this ratio. Unfortunately, such a mapping between ψ (m) /c (m) and input m s share in the total input purchases of firms is blurred by incomplete contracting and sequential production. 12

14 our assumptions imply that input production is a symmetric Cobb-Douglas function of contractible and non-contractible aspects of production. To capture differential contractibility along the value chain, we let stages differ in the (legal) costs associated with specifying these contractible aspects of production. More specifically, we denote these contracting costs by (ψ (m)) φ /µ (m) per unit of ψ (m). We shall refer to µ (m) as the level of contractibility of stage m. 13 The parameter φ > 1 captures the intuitive notion that it becomes increasingly costly to render additional aspects of production contractible. We shall assume that the firm bears the full cost of these contractible investments (perhaps by compensating suppliers for them upfront), but our results would not be affected if the firm bore only a fraction of these costs. To simplify matters, we let the marginal cost c (m) of non-contractible investments be constant along the value chain, i.e., c (m) = c for all m. In terms of the timing of events summarized in Figure 2, notice that nothing has changed except for the fact that the initial contract also specifies the profit-maximizing choice of ψ (m) along the value chain. Furthermore, once the levels of ψ (m) have been set at stage t 0, the subgame perfect equilibrium is identical to that in our previous model in which ψ (m) was assumed exogenous. This implies that the firm s optimal ownership structure along the value chain will seek to maximize the program in (7), and the solution of this problem will be characterized by Proposition 1. As shown in Section A-1 of the Online Appendix, after solving for the optimal choice of β (m) {β V, β O }, one can express firm profits net of contracting costs as: [ α (1 ρ) 1 ] ρ(1 α) π F = Θ ρ (1 α) c ρ 1 ρ Γ (β O, β V ) ψ (i) α α(1 ρ) 1 1 α di 0 0 where remember that Θ = Aθ ρ ( 1 ρ ρ 1 ρ α 1 α ) ρ α α(1 ρ) ρ ρ 1 ρ (ψ (i)) φ di, (12) µ (i) > 0, and where Γ (β O, β V ) > 0 is a function of β O and β V, as well as of α and ρ (see Section A-1 of the Online Appendix for the full expression). The choice of the profit-maximizing path of ψ (i) will thus seek to maximize π F in (12). A notable feature of equation (12) is that, leaving aside variation in the contracting costs µ (i), the marginal incentive to invest in the contractible components of input production is independent of the input s position in the value chain. This result is not entirely intuitive because, relative to a complete contracting benchmark, the degree of underinvestment in non-contractible inputs varies along the value chain and the endogenous (but coarse) choice of ownership structure does not fully correct these distortions. One might have then imagined that the choice of ψ (i) would have partly sought to remedy these remaining inefficiencies. Instead, variation in the firm s choice of contractible investments ψ (i) is solely shaped by variation in contractibility µ (i). More precisely, the first-order conditions associated with problem (12) imply that for any two inputs at stages m and m, we have: (ψ (m) /ψ (m )) φ α 1 α = µ (m) /µ (m ). For the second-order conditions of problem (12) to be satisfied, we need to assume that φ > α/ (1 α), and thus the path of ψ (m) 13 Acemoglu et al. (2007) also model input production as involving a Cobb-Douglas function of contractible and non-contractible inputs, but they capture the degree of contractibility by the elasticity of input production to the contractible components of production. In our setup with sequential production, however, such an approach precludes an analytical solution of the differential equations characterizing the equilibrium. 13

15 along the value chain is inversely related to the path of the exogenous contracting costs 1/µ (m). 14 In light of our discussion in the last section, this implies: Proposition 2. There exist thresholds m C (0, 1] and m S (0, 1] such that, in the complements case, all production stages m [0, m C ) are outsourced and all stages m [m C, 1] are integrated, while in the substitutes case, all production stages m [0, m S ) are integrated, while all stages m [m S, 1] are outsourced. Furthermore, both m C and m S are lower, the higher is the contractibility µ (m) for upstream inputs relative to downstream inputs. Figure 4 illustrates the key result of Proposition 2. Intuitively, the higher the contractibility of upstream inputs, the less firms need to rely on upstream organizational decisions as a way to counteract the distortions associated with inefficient investments by upstream suppliers. Consequently, high levels of upstream contractibility tend to reduce the set of outsourced stages whenever final-good demand is elastic or inputs are not too substitutable, while they tend to reduce the set of integrated stages whenever final-good demand is inelastic or inputs are highly substitutable. By mapping variation in ψ (m) to the degree of input contractibility, Proposition 2 helps operationalize our previous Proposition 1. In our empirical analysis, we will employ proxies for input contractibility to develop a sector-level measure of the extent to which non-contractibilities feature in upstream relative to downstream stages in the production of the output. We will then study how firm-level ownership decisions are shaped by this relative importance of upstream versus downstream contractibilities in both the complements and substitutes cases. Figure 4: The Effect of an Increase in Upstream Contractibility Sequential complements: Sequential substitutes: Outsource Integrate Integrate m C * m S * Outsource B. Heterogeneous Productivity of Final Good Producers Our model incorporates heterogeneity across final good producers in terms of their demand level A and their core productivity θ. In this section, we show how such heterogeneity shapes firm boundary choices along the value chain, in the presence of fixed organizational costs associated with vertically integrating production stages. More specifically, we shall now assume that if a firm wants to integrate a given stage i [0, 1], it needs to pay a fixed cost equal to f V > In order to facilitate a swifter transition to the empirical analysis, we shall revert back to our benchmark model with exogenous paths of ψ (i) and c (i). We will relegate most mathematical 14 The inequality φ > α/ (1 α) is necessary but not sufficient for the second-order conditions to be satisfied (see Section A-1 of the Online Appendix). 15 Our results below would continue to hold in the presence of fixed costs f O associated with outsourcing stages, as long as those fixed costs are lower than f V. 14

16 details to Section A-1 of the Online Appendix, in which we show that Proposition 1 continues to apply in this environment with fixed costs of integration. More precisely, there continue to exist thresholds m C (0, 1] and m S (0, 1] such that all production stages m [0, m C ) are outsourced and all stages m [m C, 1] are integrated in the complements case, while all production stages m [0, m S ) are integrated and all stages m [m S, 1] are outsourced in the substitutes case. Furthermore, one can still show that both m C and m S are lower, the higher is the ratio ψ (m) /c (m) for upstream inputs relative to downstream inputs. These characterization results can be obtained even though the equations determining the cutoffs m C and m S are now significantly more involved. More relevant for the purposes of this section, the equilibrium conditions defining m C and m S can also be used to study how these thresholds are affected by changes in A and θ. In Section A-1 of the Online Appendix, we show that m C is necessarily a decreasing function of the level of firm demand A or firm productivity θ. By contrast, m S is increasing in both A and θ. In words, this implies that regardless of the sign of ρ α, relatively more productive firms will tend to integrate a larger interval of production stages. The intuition behind this is simple: more productive firms find it easier to amortize the fixed cost associated with integrating more stages. In our empirical analysis, we will explore whether the observed intra-industry heterogeneity in integration choices is in accordance with these predictions, which we summarize as: Proposition 3. In the presence of fixed costs of integration, the statements in Proposition 1 continue to hold. Furthermore, the cutoff m C is decreasing in firm-level demand A and firm-level productivity θ, while m S is increasing in A and θ. Figure 5: The Effect of an Increase in Productivity of the Final Good Producer Sequential complements: Sequential substitutes: Outsource Integrate Integrate m C * m S * Outsource Figure 5 illustrates how an increase in the productivity θ of the final good producer (or an increase in firm-level demand A) affects integration choices along the value chain. The interval of integrated stages expands in both cases, but in a manner that would lead us to observe relatively more internalization of upstream stages when inputs are sequential complements, and conversely relatively more internalization of downstream stages in the substitutes case. C. Sparse Integration and Intrafirm Trade Our framework has the strong implication that the sets of integrated and outsourced stages are both connected and jointly constitute a partition of [0, 1]. As might have been expected, this strong prediction of the model is not borne out in the data. In fact, integrated stages are very sparse in our dataset and the overwhelming majority of them border with outsourced stages 15